Regal lets everyone ship code. CodeRabbit made it mergeable.

Challenge: Engineering friction in an AI-native culture

More pull requests, with less technical context

Non-technical contributors gave product-focused prompts to coding agents: build a feature that does this. The agents produced code that compiled and looked like progress, but it often carried missed edge cases, broken conventions, and assumptions that did not fit Regal's codebase. An engineer then had to review the change and explain each correction in plain language.

You end up having to leave more detailed comments than you're used to leaving on a PR. And you're communicating them to someone who may not have the same technical context.

The social cost grew with the queue

Regal needs input from the teams closest to customers. Those teams understand how customers use the product, which workflows matter, and where the experience should improve. Coding agents gave them a way to turn ideas into working changes, but the review conversation became harder.

Engineer-to-engineer reviews often run on shorthand: this should be DRY, the interface is misused, or there is too much coupling. A technical peer understands why the point matters. With a non-technical author, each comment becomes a lesson with context, rationale, and careful framing on top of the review itself.

The bottleneck was not only review time. It was the stress and confusion of delivering difficult technical feedback across teams.

When I actually listened to what people were saying, it felt more like they were complaining about stress and confusion than about time. I think the stress and confusion made it take time.

Why CodeRabbit

The first pass happens before engineering review

Non-technical contributors now iterate with CodeRabbit before a pull request reaches an engineer. Corrections, convention flags, and questions about intent come from the same review layer every time. Authors can improve their changes without turning every finding into a sensitive conversation with a colleague.

Engineers receive pull requests that have already passed through an independent first review. Their time goes to the decisions that require technical judgment instead of avoidable cleanup.

Change Stack makes complex pull requests readable

Regal prefers small, logical commits that reviewers can follow in order. AI coding agents often produce larger changes that span many files and many rounds of iteration. The original commit history may not tell the story a reviewer needs.

CodeRabbit Change Stack reorganizes a pull request into a guided, layer-by-layer walkthrough. Each layer has its own summary and relevant code ranges, with diagrams where they clarify a call flow, state change, or data model. Reviewers can follow the change by intent instead of reconstructing it from an alphabetical file list.

I'd wanted a tool like that to exist for a long time. It makes the commit history what it should have been.

PR summaries and diagrams create context before review

The first praise from Regal's engineers was not about individual review comments. It was about the pull request summaries, sequence diagrams, and visual walkthroughs that showed what was changing and how the pieces connected.

When a reviewer approaches a pull request written by a coding agent or non-technical contributor, orientation comes before judgment. CodeRabbit supplies that context so reviewers can begin with a useful mental model.

The first compliments people gave were about those diagrams. CodeRabbit's value is in how much it can help the human review — and our own review is what really drives confidence in what we ship.

Custom review rules turn migration risk into a repeatable guardrail

Regal's most important custom rule covers database migrations, where a mistake can affect a growing enterprise customer base. Harry's team codified 12 risk patterns into one CodeRabbit review rule. Dangerous operations are flagged, while other operations are restricted to maintenance windows.

The rule does not replace engineering judgment. It makes sure the known risks appear before that judgment is applied.

We're not depending on CodeRabbit to tell us the best way to write these migrations. We're looking for it to flag any risky things — serve as a backstop.

An agentic review loop that closes itself

Regal also built an automated workflow where coding agents write code, CodeRabbit reviews it, and the agents respond to the findings. The loop resolves routine issues before an engineer begins a human review.

This is a deliberate architectural choice. Coding agents act on CodeRabbit's feedback consistently, making the automated loop a meaningful contributor to the rising acceptance rate. Engineers still own the decisions that require context, architecture knowledge, and accountability.

Results: A different kind of engineering organization

CodeRabbit became the first reviewer on every pull request and handled the legwork needed for human engineers to give faster, higher-quality reviews. It surfaced code-quality concerns and departures from established practice before engineers entered the loop. Once a pull request was ready, the summary and walkthrough helped the reviewer focus on approach and architecture.

Overall comment acceptance climbed from 37% to 47.4%

Regal's overall acceptance rate reached 47.4%, with critical findings accepted at 50.3% and major findings at 46.7%. Engineers act on about half of CodeRabbit's highest-severity findings, a strong signal that the first-pass feedback is useful rather than noise.

The result is not only a faster queue. Contributors get immediate, consistent guidance. Engineers spend less energy translating routine corrections across teams. High-risk operations receive repeatable scrutiny. Human review starts with a clearer pull request and more room for judgment.

Anyone can contribute. Engineering judgment stays where it matters.

For Regal, CodeRabbit made a different kind of engineering organization possible: one where more people can contribute code, the first pass happens automatically, and engineers bring their full attention to the work that needs a human.

It just makes the organization run better that CodeRabbit exists in our workflow. And the organization would feel it if it didn't.